While digital predistortion (DPD) usually targets only the linearity performance of the radio–frequency (RF) power amplifier (PA), this work addresses more than a single PA performance metric exploiting a multi-objective optimization approach. We present a predistorer learning pro-cedure based on a constrained optimization algorithm that maximizes the RF output power, while guaranteeing a prescribed linearity level, i.e., a maximum normalized mean square error (NMSE) or adjacent-channel power ratio (ACPR). Experimental results on a Gallium Nitride (GaN) PA show that the proposed approach outperforms the classical indirect learning architecture (ILA), yet using the same predistorter structure with predetermined nonlinearity and memory orders.

GaN power amplifier digital predistortion by multi-objective optimization for maximum RF output power

Mengozzi M.
;
Gibiino G. P.
;
Angelotti A. M.;Florian C.;Santarelli A.
2021

Abstract

While digital predistortion (DPD) usually targets only the linearity performance of the radio–frequency (RF) power amplifier (PA), this work addresses more than a single PA performance metric exploiting a multi-objective optimization approach. We present a predistorer learning pro-cedure based on a constrained optimization algorithm that maximizes the RF output power, while guaranteeing a prescribed linearity level, i.e., a maximum normalized mean square error (NMSE) or adjacent-channel power ratio (ACPR). Experimental results on a Gallium Nitride (GaN) PA show that the proposed approach outperforms the classical indirect learning architecture (ILA), yet using the same predistorter structure with predetermined nonlinearity and memory orders.
Mengozzi M.; Gibiino G.P.; Angelotti A.M.; Florian C.; Santarelli A.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/805732
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